Inverse spatial distribution of brain metastases and white matter hyperintensities in advanced lung and non-lung cancer patients

The aim of this study was to test by means of a voxel-based approach the hypothesis that there is a different spatial distribution of brain metastases (BM) and white matter hyperintensities (WMH) and that the presence of WMH affects the location of BM in lung and non-lung cancer patients. Two-hundred consecutive cancer patients at first diagnosis of BM were included. Images were acquired using a 1.5 Tesla MRI system (Magnetom Avanto B13, Siemens, Erlangen, Germany). Axial FLAIR T2 weighted images and gadolinium-enhanced T1 weighted images were post-processed for segmentation, co-registration and analysis. Binary lesion masks were created for WMH and BM, using Volumes of Interest. Lesion probability maps were generated and the voxel-based lesion-symptom mapping approach was used to model each voxel and to calculate a non parametric statistics (Brunner-Munzel test) describing the differences between the groups. In the lung cancer group we found higher frequency of BM in WMH- than in WMH+ patients in the occipital lobe and the cerebellum. In contrast, BM were more frequent in the right frontal lobe in WMH+ than in WMH- patients. We suggest that there exists an inverse brain spatial distribution between WMH and BM. In lung cancer patients, the presence of WMH seems to shift the distribution of BM toward locations different than what it is expected based on primary tumor

Spatial brain distribution of intra-axial metastatic lesions in breast and lung cancer patients

The frequency of the diagnosis of brain metastases has increased in recent years, probably due to an increased diagnostic sensitivity. Site predilection of brain lesions in oncological patients at the time of onset, may suggest mechanisms of brain-specific vulnerability to metastasis. The aim of the study is to determine the spatial distribution of intra-axial brain metastases by using voxel-wise statistics in breast and lung cancer patients. For this retrospective cross-sectional study, clinical data and MR imaging of 864 metastases at first diagnosis in 114 consecutive advanced cancer patients from 2006 to 2011 were included. Axial post-gadolinium T1 weighted images were registered to a standard template. Binary lesion masks were created after segmentation of volumes of interest. The voxel-based lesion-symptom mapping approach was used to calculate a t statistic describing the differences between groups. It was found that the lesions were more likely to be located in the parieto-occipital lobes and cerebellum for the total cohort and for the non small cell lung cancer group, and in the cerebellum for the breast cancer group. The voxel-wise inter-group comparisons showed the largest significant clusters in the cerebellum for the breast cancer group (p < 0.0008) and in the occipital lobe (p = 0.02) and cerebellum (p = 0.02) for the non small cell lung cancer group. We conclude a non-uniform distribution of metastatic brain lesions in breast and lung cancer patients that suggest differential vulnerability to metastasis in the different regions of the brain

Deep learning algorithm trained with COVID-19 pneumonia also identifies immune checkpoint inhibitor therapy-related pneumonitis

Background: Coronavirus disease 2019 (COVID-19) pneumonia and immune checkpoint inhibitor (ICI) therapy-related pneumonitis share common features. The aim of this study was to determine on chest computed tomography (CT) images whether a deep convolutional neural network algorithm is able to solve the challenge of differential diagnosis between COVID-19 pneumonia and ICI therapy-related pneumonitis. Methods: We enrolled three groups: a pneumonia-free group (n = 30), a COVID-19 group (n = 34), and a group of patients with ICI therapy-related pneumonitis (n = 21). Computed tomography images were analyzed with an artificial intelligence (AI) algorithm based on a deep convolutional neural network structure. Statistical analysis included the Mann– Whitney U test (significance threshold at p < 0.05) and the receiver operating characteristic curve (ROC curve). Results: The algorithm showed low specificity in distinguishing COVID-19 from ICI therapy-related pneumonitis (sensitivity 97.1%, specificity 14.3%, area under the curve (AUC) = 0.62). ICI therapy-related pneumonitis was identified by the AI when compared to pneumonia-free controls (sensitivity = 85.7%, specificity 100%, AUC = 0.97). Conclusions: The deep learning algorithm is not able to distinguish between COVID-19 pneumonia and ICI therapy-related pneumonitis. Awareness must be increased among clinicians about imaging similarities between COVID-19 and ICI therapy related pneumonitis. ICI therapy-related pneumonitis can be applied as a challenge population for cross-validation to test the robustness of AI models used to analyze interstitial pneumonias of variable etiology